A data scientist needs to be Critical and always on a lookout of something that misses others. So here are some advices that one can include in day to day data science work to be better at their work: 1. Beware of the Clean Data Syndrome You need to ask yourself questions even before you start working on the data. Does this data make sense? Falsely assuming that the data is clean could lead you towards wrong Hypotheses.
As a data scientist I believe that a lot of work has to be done before Classification/Regression/Clustering methods are applied to the data you get. The data which may be messy, unwieldy and big. So here are the list of algorithms that helps a data scientist to make better models using the data they have: 1. Sampling Algorithms. In case you want to work with a sample of data.
This is a post which deviates from my pattern fo blogs that I have wrote till now but I found that Finance also uses up a lot of Statistics. So it won’t be a far cry to put this on my blog here. I recently started investing in Mutual funds so thought of rersearching the area before going all in. Here is the result of some of my research.
Last time I wrote an article on MCMC and how they could be useful. We learned how MCMC chains could be used to simulate from a random variable whose distribution is partially known i.e. we don’t know the normalizing constant. So MCMC Methods may sound interesting to some (for these what follows is a treat) and for those who don’t really appreciate MCMC till now, I hope I will be able to pique your interest by the end of this blog post.
The things that I find hard to understand push me to my limits. One of the things that I have always found hard is Markov Chain Monte Carlo Methods. When I first encountered them, I read a lot about them but mostly it ended like this. The meaning is normally hidden in deep layers of Mathematical noise and not easy to decipher. This blog post is intended to clear up the confusion around MCMC methods, Know what they are actually useful for and Get hands on with some applications.